In SER, finding the better acoustic feature set is still an ongoing active sub-field of research in the domain [1]}.
Most of the handcrafted sets [2]}, [3]} intend to describe prosody in the signal, with low level descriptors (LLDs) capturing intensity, intonation, rhythm or voice quality. Another approach is to extract spectral features: mel frequency cepstral coefficients (MFCCs) are clearly the most often used as they are robust to noisy signals even if they have not been designed for prosody nor emotion.
Recently wav2vec [4]} and Audio AlBERT [5]} were introduced in ASR and speaker identification as one of the first pre-trained approaches to extract context dependent features from raw signals for ASR tasks.
In our study, we propose to investigate the use of a pre-trained wav2vec model to extract acoustic features in order to retrieve the continuous emotional state of the speaker.
